A successful concept for measuring non-planarity of graphs: the crossing number

AbstractThis paper surveys how the concept of crossing number, which used to be familiar only to a limited group of specialists, emerges as a significant graph parameter. This paper has dual purposes: first, it reviews foundational, historical, and philosophical issues of crossing numbers, second, it shows a new lower bound for crossing numbers. This new lower bound may be helpful in estimating crossing numbers

Copula models for epidemiological research and practice

Investigating associations between random variables (rvs) is one of many topics in the heart of statistical science. Graphical displays show emerging patterns between rvs, and the strength of their association is conventionally quantified via correlation coefficients. When two or more of these rvs are thought of as outcomes, their association is governed by a joint probability distribution function (pdf). When the joint pdf is bivariate normal, scalar correlation coefficients will produce a satisfactory summary of the association, otherwise alternative measures are needed. Local dependence functions, together with their corresponding graphical displays, quantify and show how the strength of the association varies across the span of the data. Additionally, the multivariate distribution function can be explicitly formulated and explored. Copulas model joint distributions of varying shapes by combining the separate (univariate) marginal cumulative distribution functions of each rv under a specified correlation structure. Copula models can be used to analyse complex relationships and incorporate covariates into their parameters. Therefore, they offer increased flexibility in modelling dependence between rvs. Copula models may also be used to construct bivariate analogues of centiles, an application for which few references are available in the literature though it is of particular interest for many paediatric applications. Population centiles are widely used to highlight children or adults who have unusual univariate outcomes. Whilst the methodology for the construction of univariate centiles is well established there has been very little work in the area of bivariate analogues of centiles where two outcomes are jointly considered. Conditional models can increase the efficiency of centile analogues in detection of individuals who require some form of intervention. Such adjustments can be readily incorporated into the modelling of the marginal distributions and of the dependence parameter within the copula model

Liberalization, Growth, and Financial Crises: Lessons from Mexico and the Developing World

Although the case for trade liberalization is now well established, the case for financial liberalization is not, because the latter is associated with lending booms and crises. Some critics invoke as evidence the recent weak growth of Mexico, a prominent liberalizer. We argue that liberalization is beneficial despite the occurrence of crises. First, we show that financial liberalization has typically followed trade liberalization, and that both have led to faster growth, despite more frequent booms and busts. Second, we present a model that shows why, in countries with severe credit market imperfections, liberalization leads to faster growth and, as a by-product, to financial fragility. Third, comparing Mexico with this international norm, we show that liberalization and NAFTA have induced faster growth and investment but have not been enough: lack of structural reform and a protracted credit crunch generated bottlenecks that blocked further growth and led to a slowdown in exports.Mexico, bank, growth, financial crisis, macroeconomics

Data visualization and health econometrics

This monograph focuses on the principles and practice of data visualization and statistical graphics and how these can enhance empirical analysis of health care costs and outcomes. The scope is limited to non-normal but continuous outcomes. The methods and applications used here are limited to cross sectional data

Resting state fMRI as a marker for progression from mild cognitive impairment to Alzheimer’s disease

The arc of progression in the most common neurological aiction known as Alzheimer's Disease (AD), is characterized by a prodromal stage of Mild Cognitive Impairment (MCI). MCI subjects have traditionally been diagnosed with a battery of cognitive tests, but in recent times two good biomarker predictors of incipient AD have been identied. The cerebrospinal uid levels of the protein residues amyloid-beta and phosphorylated tau can be quantied and directly relate to the imprint of associated pathologies in the brain. This work aims to elucidate the impact of tau- and amyloid-related pathologies on the functional networks of the brain, as gauged by a resting-state fMRI connectivity analysis. In this context, we aim to identify optimal model parameters that yield maximal contrast between subspecies of MCI and healthy controls, such as the most sensitive frequency interval for the Blood Oxygenation Level Dependent (BOLD) time-series and the resolution of whole-brain parcellation schemes. The connectivity analysis exposes the impact of biomarker pathology and outlines a tentative progression pattern, relating the decline of functional connectivity to increasingly pathological levels of biomarkers. A progression hypothesisis proposed, reviewing pattern progression in the light of neuronal communication breakdown and phase-lag. Furthermore, failure of key hubs are identied using graph theoretical centrality measures and the relative group separations with connectivity pattern is evaluated by means of support-vector machines. Relative healthy controls, MCI with non-pathological CSF levels of biomarkers exhibit a widespread pattern of reduced connectivity, likely due to a mix many dementia subtypes. MCI subjects with pathological amyloid CSF levels but normal values of tau, has a large set of failing links converg- ing on crucial network hubs: thalamus, caudate nucleus and putamen, and are additionally aected in key regions such as hippocampus. This nding supports the view of Alzheimer's progression in terms of global disconnection syndrome by failing hub regions. Furthermore, these patterns are man- ifested in relevant graph theoretical centrality measures. MCI with pathological levels of both CSF biomarkers produce the strongest contrast relative healthy controls, involving reduced connectivity beteween parietal and frontal areas, but also implicating areas linked with cognitive decline, such as hippocampus and posterior cingulate cortex. The similarity of this contrast to that of controls vs. Alzheimer's subjects, indicates the presence of a functional progression pattern with two biomarker levels. Our ndings merit further investigation of the biomarker progression line using larger cohorts further stratied with cognitive test scores

Liberalization, Growth, and Financial Crises: Lessons from Mexico and the Developing World

and the Developing World By now there is widespread agreement that trade liberalization enhances growth. No such agreement exists, however, on the growth-enhancing effects of financial liberalization, in large part because it is associated with risky capital flows, lending booms, and crises. The Mexi-can experience is often considered a prime example of what can go wrong with liberalization. Mexico liberalized its trade and finance and entered the North American Free Trade Agreement (NAFTA), yet despite these reforms, Mexico’s growth performance has been unremarkable in com-parison with that of its peers. A particularly worrisome development is that, since 2000, there has been a slowdown in Mexico’s exports. That financial liberalization is bad for growth because it leads to crises is the wrong lesson to draw. Our empirical analysis shows that, in coun-tries with severe credit market imperfections, financial liberalization leads to more rapid growth, but also to a higher incidence of crises. I

Optimization of storage and picking systems in warehouses

La croissance du commerce électronique exige une hausse des performances des systèmes d'entreposage, qui sont maintenant repensés pour faire face à un volume massif de demandes à être satisfait le plus rapidement possible. Le système manuel et le système à robots mobile (SRM) sont parmi les plus utilisés pour ces activités. Le premier est un système centré sur l'humain pour réaliser des opérations complexes que les robots actuels ne peuvent pas effectuer. Cependant, les nouvelles générations de robots autonomes mènent à un remplacement progressif par le dernier pour augmenter la productivité. Quel que soit le système utilisé, plusieurs problèmes interdépendants doivent être résolus pour avoir des processus de stockage et de prélèvement efficaces. Les problèmes de stockage concernent les décisions d'où stocker les produits dans l'entrepôt. Les problèmes de prélèvement incluent le regroupement des commandes à exécuter ensemble et les itinéraires que les cueilleurs et les robots doivent suivre pour récupérer les produits demandés. Dans le système manuel, ces problèmes sont traditionnellement résolus à l'aide de politiques simples que les préparateurs peuvent facilement suivre. Malgré l'utilisation de robots, la même stratégie de solution est répliquée aux problèmes équivalents trouvés dans le SRM. Dans cette recherche, nous étudions les problèmes de stockage et de prélèvement rencontrés lors de la conception du système manuel et du SRM. Nous développons des outils d'optimisation pour aider à la prise de décision pour mettre en place leurs processus, en améliorant les mesures de performance typiques de ces systèmes. Certains problèmes traditionnels sont résolus avec des techniques améliorées, tandis que d'autres sont intégrés pour être résolus ensemble au lieu d'optimiser chaque sous-système de manière indépendante. Nous considérons d'abord un système manuel avec un ensemble connu de commandes et intégrons les décisions de stockage et de routage. Le problème intégré et certaines variantes tenant compte des politiques de routage communes sont modélisés mathématiquement. Une métaheuristique générale de recherche de voisinage variable est présentée pour traiter des instances de taille réelle. Des expériences attestent de l'efficience de la métaheuristique proposée par rapport aux modèles exacts et aux politiques de stockage communes. Lorsque les demandes futures sont incertaines, il est courant d'utiliser une stratégie de zonage qui divise la zone de stockage en zones et attribue les produits les plus demandés aux meilleures zones. Les tailles des zones sont à déterminer. Généralement, des dimensions arbitraires sont choisies, mais elles ignorent les caractéristiques de l'entrepôt et des demandes. Nous abordons le problème de dimensionnement des zones pour déterminer quels facteurs sont pertinents pour choisir de meilleures tailles de zone. Les données générées à partir de simulations exhaustives sont utilisées pour trainer quatre modèles de régression d'apprentissage automatique - moindres carrés ordinaire, arbre de régression, forêt aléatoire et perceptron multicouche - afin de prédire les dimensions optimales des zones en fonction de l'ensemble de facteurs pertinents identifiés. Nous montrons que tous les modèles entraînés suggèrent des dimensions sur mesure des zones qui performent meilleur que les dimensions arbitraires couramment utilisées. Une autre approche pour résoudre les problèmes de stockage pour le système manuel et pour le SRM considère les corrélations entre les produits. L'idée est que les produits régulièrement demandés ensemble doivent être stockés près pour réduire les coûts de routage. Cette politique de stockage peut être modélisée comme une variante du problème d'affectation quadratique (PAQ). Le PAQ est un problème combinatoire traditionnel et l'un des plus difficiles à résoudre. Nous examinons les variantes les plus connues du PAQ et développons une puissante métaheuristique itérative de recherche tabou mémétique en parallèle capable de les résoudre. La métaheuristique proposée s'avère être parmi les plus performantes pour le PAQ et surpasse considérablement l'état de l'art pour ses variantes. Les SRM permettent de repositionner facilement les pods d'inventaire pendant les opérations, ce qui peut conduire à un processus de prélèvement plus économe en énergie. Nous intégrons les décisions de repositionnement des pods à l'attribution des commandes et à la sélection des pods à l'aide d'une stratégie de prélèvement par vague. Les pods sont réorganisés en tenant compte du moment et de l'endroit où ils devraient être demandés au futur. Nous résolvons ce problème en utilisant la programmation stochastique en tenant compte de l'incertitude sur les demandes futures et suggérons une matheuristique de recherche locale pour résoudre des instances de taille réelle. Nous montrons que notre schéma d'approximation moyenne de l'échantillon est efficace pour simuler les demandes futures puisque nos méthodes améliorent les solutions trouvées lorsque les vagues sont planifiées sans tenir compte de l'avenir. Cette thèse est structurée comme suit. Après un chapitre d'introduction, nous présentons une revue de la littérature sur le système manuel et le SRM, et les décisions communes prises pour mettre en place leurs processus de stockage et de prélèvement. Les quatre chapitres suivants détaillent les études pour le problème de stockage et de routage intégré, le problème de dimensionnement des zones, le PAQ et le problème de repositionnement de pod. Nos conclusions sont résumées dans le dernier chapitre.The rising of e-commerce is demanding an increase in the performance of warehousing systems, which are being redesigned to deal with a mass volume of demands to be fulfilled as fast as possible. The manual system and the robotic mobile fulfillment system (RMFS) are among the most commonly used for these activities. The former is a human-centered system that handles complex operations that current robots cannot perform. However, newer generations of autonomous robots are leading to a gradual replacement by the latter to increase productivity. Regardless of the system used, several interdependent problems have to be solved to have efficient storage and picking processes. Storage problems concern decisions on where to store products within the warehouse. Picking problems include the batching of orders to be fulfilled together and the routes the pickers and robots should follow to retrieve the products demanded. In the manual system, these problems are traditionally solved using simple policies that pickers can easily follow. Despite using robots, the same solution strategy is being replicated to the equivalent problems found in the RMFS. In this research, we investigate storage and picking problems faced when designing manual and RMFS warehouses. We develop optimization tools to help in the decision-making process to set up their processes and improve typical performance measures considered in these systems. Some classic problems are solved with improved techniques, while others are integrated to be solved together instead of optimizing each subsystem sequentially. We first consider a manual system with a known set of orders and integrate storage and routing decisions. The integrated problem and some variants considering common routing policies are modeled mathematically. A general variable neighborhood search metaheuristic is presented to deal with real-size instances. Computational experiments attest to the effectiveness of the metaheuristic proposed compared to the exact models and common storage policies. When future demands are uncertain, it is common to use a zoning strategy to divide the storage area into zones and assign the most-demanded products to the best zones. Zone sizes are to be determined. Commonly, arbitrary sizes are chosen, which ignore the characteristics of the warehouse and the demands. We approach the zone sizing problem to determine which factors are relevant to choosing better zone sizes. Data generated from exhaustive simulations are used to train four machine learning regression models - ordinary least squares, regression tree, random forest, and multilayer perceptron - to predict the optimal zone sizes given the set of relevant factors identified. We show that all trained models suggest tailor-made zone sizes with better picking performance than the arbitrary ones commonly used. Another approach to solving storage problems, both in the manual and RMFS, considers the correlations between products. The idea is that products constantly demanded together should be stored closer to reduce routing costs. This storage policy can be modeled as a quadratic assignment problem (QAP) variant. The QAP is a traditional combinatorial problem and one of the hardest to solve. We survey the most traditional QAP variants and develop a powerful parallel memetic iterated tabu search metaheuristic capable of solving them. The proposed metaheuristic is shown to be among the best performing ones for the QAP and significantly outperforms the state-of-the-art for its variants. The RMFS allows easy repositioning of inventory pods during operations that can lead to a more energy-efficient picking process. We integrate pod repositioning decisions with order assignment and pod selection using a wave picking strategy such that pods are parked after being requested considering when and where they are expected to be requested next. We solve this integrated problem using stochastic programming considering the uncertainty about future demands and suggest a local search matheuristic to solve real-size instances. We show that our sample average approximation scheme is effective to simulate future demands since our methods improve solutions found when waves are planned without considering the future demands. This thesis is structured as follows. After an introductory chapter, we present a literature review on the manual and RMFS, and common decisions made to set up their storage and picking processes. The next four chapters detail the studies for the integrated storage and routing problem, the zone sizing problem, the QAP, and the pod repositioning problem. Our findings are summarized in the last chapter